Automatic visual inspection of thermoelectric metal pipes


This paper presents the main aspects of the design of an image acquisition and processing approach that can be inserted into thermoelectric metal pipe systems and travel inside the pipes to capture images from the inner surface of such pipes for further analysis. After the image capture, a preprocessing is applied based on iris recognition, which transforms the image from a Cartesian coordinate system to a polar coordinate system, which allows a better texture analysis of the internal surface of the pipe. The extracted information is used to train a classifier capable of automatically identifying segments that present some type of corrosion or defects. The experimental results in a dataset of 6150 images using two textural features have shown that the proposed classification approach can achieve accuracy between 96 and 98% in the test set.

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The authors of this work acknowledge the ANEEL for the Research and Development program, the Neonergia Group, for the project funding and the LACTEC for the infra structure and support.

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Correspondence to Daniel Vriesman.

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Vriesman, D., Britto, A.S., Zimmer, A. et al. Automatic visual inspection of thermoelectric metal pipes. SIViP 13, 975–983 (2019).

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  • Visual inspection
  • Texture
  • Fusion of features
  • Automatic inspection